my-little-markov-model

A Twitter bot that uses Markov models to generate new tweets based on different corpuses.

Requirements

The requirements for package installation is encompassed in the requirements.txt for pip users and and environment.yml for Conda users.

To set up the environment in Conda run:

conda env create -n complaints -f environment.yml

Or for pip:

pip install -r requirements.txt

To update the environment.yml file you can run the following command if using a Conda environment:

conda env export > environment.yml

To output a pip formatted requirements.txt use the following command to generate one from a Conda environment:

pip list --format=freeze > requirements.txt

If running on Windows must remove pywin32==304 from requirements.txt before deployment.

Local Development

To build and run the application locally you can use the following make targets:

make docker-build
make docker-run

Deployment

To deploy and update the running system ensure you have the Google Cloud CLI installed following these instructions: Install the gcloud CLI.

You will need access to the my-little-markov GCP project.

Then run the make target:

make deploy-prod

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience